Online Tibetan Handwriting Recognition for Large Character Set on New Databases

Author(s):  
Weilan Wang ◽  
Zhengjiang Li ◽  
Zhengqi Cai ◽  
Xiaobao Lv ◽  
Caike Zhaxi ◽  
...  

The online handwriting recognition of Tibetan characters is still in its infancy. For further research, an online handwriting database of large Tibetan character set was developed, and a recognition research was carried out on this database as a baseline result. The Northwest Minzu University Online Tibetan Handwriting Database (NMU-OLTHWDB) contains 7240 different types of characters, and the sample number in each type is 5000. The total number of samples is [Formula: see text]. The database covers Tibetan Character Collection, Information Technology Tibetan Coded Character set (Extension Set A), and Information Technology Tibetan Coded Character set (Extension Set B). The characters in the database are composed of 170 types of different components. We studied the online handwritten Tibetan recognition software also, and the character feature extraction, classifier training, and the statistics and analysis of the recognition results on the test set were mainly introduced. The character features included the direction attribute coefficients and spatial combination, and the feature matrix was compressed by Linear Discriminate Analysis (LDA). A quick classifier was designed by a modified quadratic discriminate function (QMQDF), and was trained with 4500 sets of samples. In the large character set, the recognition rates of top 1, top 3, top 5, and top 10 were 75.2%, 89.56%, 93.02%, and 95.96%, respectively. Moreover, an online handwriting recognition system for Tibetan large character set was designed with good performance.

2019 ◽  
Vol 8 (2) ◽  
pp. 2283-2288

Online handwriting recognition or character recognition is the process in which a handwritten message is recognized by processing the handwritten data. It is the way toward changing over manually written characters to machine design. In penmanship, the strokes are made out of two arrange follows in the middle of pen down and pen up marks. Wide scope of highlights is extricated to play out thse acknowledgment. A complete internet hand-written recognition system for Indian language such as Telugu that addresses the ambiguities in separation just as recognition of buttons the recognition relies on conceptual model of penmanship structure joined with either a prejudicial model for stroke command. Such a methodology be able to flawlessly incorporate language and content data in the reproductive model then manage comparative and non-comparable strokes utilizing the single discriminative stroke grouping model. In this examination, we are utilizing disparate Legendre Sobolev conditions with the assistance of AI model, to such an extent that accomplishes 99.65% precision and improved the condition of craftsmanship esteem.


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